Fuzzing

Concept

Fuzzing is a testing technique that repeatedly runs a target with modified inputs to uncover bugs, commonly security vulnerabilities in input-parsing code. Recent research described in the provided sources applies machine learning to generate fuzzing grammars and prompt-based generation to produce fuzz drivers; a related RISC-V UVM verification work also includes fuzzing in its experimental evaluation.

First seen 5/27/2026

Last seen 6/6/2026

Evidence 6 chunks

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Overview

Fuzzing is a testing technique in which an application is repeatedly exercised with modified, or “fuzzed,” inputs. In the software-security setting described by Learn&Fuzz, its goal is to find security vulnerabilities in input-parsing code.

Input-structure learning and fuzzing

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RELATIONSHIPS

3 connections

Cross-level processor verification via endless randomized instruction stream generation with coverage-guided aging compares with → 85% 2e

The paper discusses fuzzing as a related technique compared to its approach.

UVM Based Design Verification of a RISC-V CPU Core ← uses 90% 1e

The paper includes fuzzing as part of its experimental evaluation.

OSS-Fuzz ← implements 95% 1e

OSS-Fuzz is an industry-based fuzzing platform

LINKED ENTITIES

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UVM Based Design Verification of a RISC-V CPU Core USES Extracted graph relationship

CITATIONS

7 sources

7 citations — click to expand

[1] Fuzzing repeatedly tests an application with modified or fuzzed inputs to find security vulnerabilities in input-parsing code. Learn&Fuzz: Machine Learning for Input Fuzzing

[2] Learn&Fuzz describes a tension between learning well-formed input structure and fuzzing that breaks structure to cover unexpected code paths and find bugs. Learn&Fuzz: Machine Learning for Input Fuzzing

[3] Learn&Fuzz uses sample inputs and neural-network-based statistical machine learning to automate generation of an input grammar for fuzzing, with a PDF/Microsoft Edge parser case study and a learned-probability-distribution-guided fuzzing algorithm. Learn&Fuzz: Machine Learning for Input Fuzzing

[4] Crafting high-quality fuzz drivers is time-consuming and requires deep library understanding. Prompt Fuzzing for Fuzz Driver Generation

[5] PromptFuzz is a coverage-guided fuzzer for prompt fuzzing that iteratively generates fuzz drivers and uses instructive program generation, erroneous program validation, coverage-guided prompt mutation, and constrained fuzzer scheduling. Prompt Fuzzing for Fuzz Driver Generation

[6] PromptFuzz was evaluated on 14 real-world libraries; its generated fuzz drivers achieved 1.61× and 1.63× higher branch coverage than OSS-Fuzz and Hopper, and detected 33 genuine new bugs among 49 crashes, with 30 confirmed. Prompt Fuzzing for Fuzz Driver Generation

[7] UVM Based Design Verification of a RISC-V CPU Core includes a Fuzzing subsection in Chapter 5, Experimental evaluation. [PDF] UVM based design veri cation of a RISC-V CPU core - POLITesi